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Merged16 Qwen2.5 32 by Nitish035

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Merged16 Qwen2.5 32 is an open-source language model by Nitish035. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.19.

  4bit   Autotrain compatible   Conversational   En   Endpoints compatible   Instruct   Quantized   Qwen2   Region:us   Safetensors   Sharded   Tensorflow   Unsloth
Model Card on HF 🤗: https://huggingface.co/Nitish035/merged16_qwen2.5-32 

Merged16 Qwen2.5 32 Benchmarks

LLME Score: 0.19437
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Merged16 Qwen2.5 32 (Nitish035/merged16_qwen2.5-32)
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Merged16 Qwen2.5 32 Parameters and Internals

LLM NameMerged16 Qwen2.5 32
Repository 🤗https://huggingface.co/Nitish035/merged16_qwen2.5-32 
Base Model(s)  unsloth/qwen2.5-7b-instruct-bnb-4bit   unsloth/qwen2.5-7b-instruct-bnb-4bit
Model Size7b
Required VRAM15.2 GB
Updated2025-09-20
MaintainerNitish035
Model Typeqwen2
Instruction-BasedYes
Model Files  4.9 GB: 1-of-4   4.9 GB: 2-of-4   4.3 GB: 3-of-4   1.1 GB: 4-of-4
Supported Languagesen
Quantization Type4bit
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.52.4
Tokenizer ClassQwen2Tokenizer
Padding Token<|im_end|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

Best Alternatives to Merged16 Qwen2.5 32

Best Alternatives
Context / RAM
Downloads
Likes
Qwen2.5 7B Instruct 1M 4bit986K / 4.3 GB39611
Qwen2.5 7B Instruct 1M 8bit986K / 8.1 GB864
Qwen2.5 7B Instruct 1M 6bit986K / 6.2 GB522
...B Instruct 1M Unsloth Bnb 4bit986K / 7.5 GB403
Alisia 7B Instruct V1128K / 15.4 GB1142
...5 7B Instruct SFT Meme LoRA V7128K / 15.2 GB50
Gte Qwen2 7B Instruct 4bit DWQ128K / 4.3 GB1163
Qwen2.5 7B Legal Chatbot Exp132K / 15.2 GB2620
Crazy AI Model32K / 15.2 GB2411
Qwen2.5 7B Legal Id Sft32K / 15.2 GB1260
Note: green Score (e.g. "73.2") means that the model is better than Nitish035/merged16_qwen2.5-32.

Rank the Merged16 Qwen2.5 32 Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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